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dat<-read.csv('2019-06-13-exam-pa-data-file.csv')
summary(dat)## Crash_Score year Month Time_of_Day
## Min. : 0.010 Min. :2014 Min. : 1.00 Min. :1.000
## 1st Qu.: 3.540 1st Qu.:2015 1st Qu.: 3.00 1st Qu.:3.000
## Median : 5.660 Median :2016 Median : 7.00 Median :4.000
## Mean : 6.567 Mean :2016 Mean : 6.56 Mean :4.034
## 3rd Qu.: 8.600 3rd Qu.:2017 3rd Qu.:10.00 3rd Qu.:5.000
## Max. :53.070 Max. :2019 Max. :12.00 Max. :6.000
##
## Rd_Feature Rd_Character Rd_Class
## DRIVEWAY : 2373 CURVE-GRADE : 643 OTHER : 9960
## INTERSECTION: 6702 CURVE-LEVEL : 725 STATE HWY:10603
## NONE :13025 CURVE-OTHER : 239 US HWY : 2574
## OTHER : 259 OTHER : 13
## RAMP : 778 STRAIGHT-GRADE: 2622
## STRAIGHT-LEVEL:18215
## STRAIGHT-OTHER: 680
## Rd_Configuration Rd_Surface
## ONE-WAY : 1496 COARSE ASPHALT : 1997
## TWO-WAY-NO-MEDIAN :12076 CONCRETE : 692
## TWO-WAY-PROTECTED-MEDIAN : 2627 GROOVED CONCRETE: 371
## TWO-WAY-UNPROTECTED-MEDIAN: 6882 OTHER : 70
## UNKNOWN : 56 SMOOTH ASPHALT :20007
##
##
## Rd_Conditions Light Weather
## DRY :19262 DARK-LIT : 3219 CLEAR :17393
## ICE-SNOW-SLUSH: 322 DARK-NOT-LIT: 708 CLOUDY: 3234
## OTHER : 134 DAWN : 140 OTHER : 85
## WET : 3419 DAYLIGHT :18262 RAIN : 2230
## DUSK : 602 SNOW : 195
## OTHER : 206
##
## Traffic_Control Work_Area
## NONE :14028 NO :22823
## OTHER : 228 YES: 314
## SIGNAL : 6352
## STOP-SIGN: 2269
## YIELD : 260
##
## vars<-colnames(dat)[5:14]
for (i in vars){
table <- as.data.frame(table(dat[,i]))
max <- which.max(table[,2])
level.name <- as.character(table[max,1])
dat[,i] <- relevel(dat[,i], ref = level.name)
}
summary(dat)## Crash_Score year Month Time_of_Day
## Min. : 0.010 Min. :2014 Min. : 1.00 Min. :1.000
## 1st Qu.: 3.540 1st Qu.:2015 1st Qu.: 3.00 1st Qu.:3.000
## Median : 5.660 Median :2016 Median : 7.00 Median :4.000
## Mean : 6.567 Mean :2016 Mean : 6.56 Mean :4.034
## 3rd Qu.: 8.600 3rd Qu.:2017 3rd Qu.:10.00 3rd Qu.:5.000
## Max. :53.070 Max. :2019 Max. :12.00 Max. :6.000
##
## Rd_Feature Rd_Character Rd_Class
## NONE :13025 STRAIGHT-LEVEL:18215 STATE HWY:10603
## DRIVEWAY : 2373 CURVE-GRADE : 643 OTHER : 9960
## INTERSECTION: 6702 CURVE-LEVEL : 725 US HWY : 2574
## OTHER : 259 CURVE-OTHER : 239
## RAMP : 778 OTHER : 13
## STRAIGHT-GRADE: 2622
## STRAIGHT-OTHER: 680
## Rd_Configuration Rd_Surface
## TWO-WAY-NO-MEDIAN :12076 SMOOTH ASPHALT :20007
## ONE-WAY : 1496 COARSE ASPHALT : 1997
## TWO-WAY-PROTECTED-MEDIAN : 2627 CONCRETE : 692
## TWO-WAY-UNPROTECTED-MEDIAN: 6882 GROOVED CONCRETE: 371
## UNKNOWN : 56 OTHER : 70
##
##
## Rd_Conditions Light Weather
## DRY :19262 DAYLIGHT :18262 CLEAR :17393
## ICE-SNOW-SLUSH: 322 DARK-LIT : 3219 CLOUDY: 3234
## OTHER : 134 DARK-NOT-LIT: 708 OTHER : 85
## WET : 3419 DAWN : 140 RAIN : 2230
## DUSK : 602 SNOW : 195
## OTHER : 206
##
## Traffic_Control Work_Area
## NONE :14028 NO :22823
## OTHER : 228 YES: 314
## SIGNAL : 6352
## STOP-SIGN: 2269
## YIELD : 260
##
## Task 1
library(ggplot2)## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlangp<-ggplot(dat,aes(x=Crash_Score))+geom_histogram()
p## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
vars <- colnames(dat)[colnames(dat)!="Crash_Score"]
for (i in vars) {
plot <- ggplot(dat, aes(x=as.factor(dat[,i]),y=Crash_Score)) + geom_boxplot() + labs(x=i)
print(plot)
}
library(plyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionfor (i in vars) {
print(i)
x <- dat %>% group_by_(i)%>%summarise(mean=mean(Crash_Score),
median=median(Crash_Score),
n = n())
print(x)
}## [1] "year"## Warning: group_by_() is deprecated.
## Please use group_by() instead
##
## The 'programming' vignette or the tidyeval book can help you
## to program with group_by() : https://tidyeval.tidyverse.org
## This warning is displayed once per session.## # A tibble: 6 x 4
## year mean median n
## <int> <dbl> <dbl> <int>
## 1 2014 6.62 5.77 3928
## 2 2015 6.53 5.58 4402
## 3 2016 6.62 5.68 4651
## 4 2017 6.58 5.66 4558
## 5 2018 6.52 5.63 4833
## 6 2019 6.42 5.6 765
## [1] "Month"
## # A tibble: 12 x 4
## Month mean median n
## <int> <dbl> <dbl> <int>
## 1 1 6.56 5.76 2052
## 2 2 6.67 5.66 1947
## 3 3 6.54 5.67 1887
## 4 4 6.61 5.68 1705
## 5 5 6.52 5.71 2013
## 6 6 6.52 5.53 1864
## 7 7 6.51 5.65 1671
## 8 8 6.53 5.7 1926
## 9 9 6.54 5.65 1843
## 10 10 6.65 5.64 2186
## 11 11 6.48 5.57 1972
## 12 12 6.66 5.65 2071
## [1] "Time_of_Day"
## # A tibble: 6 x 4
## Time_of_Day mean median n
## <int> <dbl> <dbl> <int>
## 1 1 5.87 4.86 808
## 2 2 6.38 5.44 1627
## 3 3 6.63 5.7 4827
## 4 4 6.55 5.71 6939
## 5 5 6.74 5.79 6579
## 6 6 6.38 5.51 2357
## [1] "Rd_Feature"
## # A tibble: 5 x 4
## Rd_Feature mean median n
## <fct> <dbl> <dbl> <int>
## 1 NONE 6.38 5.45 13025
## 2 DRIVEWAY 6.23 5.52 2373
## 3 INTERSECTION 7.08 6.2 6702
## 4 OTHER 6.35 5.25 259
## 5 RAMP 6.29 5.50 778
## [1] "Rd_Character"
## # A tibble: 7 x 4
## Rd_Character mean median n
## <fct> <dbl> <dbl> <int>
## 1 STRAIGHT-LEVEL 6.60 5.67 18215
## 2 CURVE-GRADE 6.09 5.06 643
## 3 CURVE-LEVEL 6.15 5.06 725
## 4 CURVE-OTHER 6.78 5.44 239
## 5 OTHER 5.66 4.16 13
## 6 STRAIGHT-GRADE 6.61 5.86 2622
## 7 STRAIGHT-OTHER 6.41 5.63 680
## [1] "Rd_Class"
## # A tibble: 3 x 4
## Rd_Class mean median n
## <fct> <dbl> <dbl> <int>
## 1 STATE HWY 6.90 5.98 10603
## 2 OTHER 6.15 5.35 9960
## 3 US HWY 6.79 5.65 2574
## [1] "Rd_Configuration"
## # A tibble: 5 x 4
## Rd_Configuration mean median n
## <fct> <dbl> <dbl> <int>
## 1 TWO-WAY-NO-MEDIAN 6.40 5.53 12076
## 2 ONE-WAY 6.26 5.38 1496
## 3 TWO-WAY-PROTECTED-MEDIAN 6.82 5.76 2627
## 4 TWO-WAY-UNPROTECTED-MEDIAN 6.84 5.96 6882
## 5 UNKNOWN 6.30 5.46 56
## [1] "Rd_Surface"
## # A tibble: 5 x 4
## Rd_Surface mean median n
## <fct> <dbl> <dbl> <int>
## 1 SMOOTH ASPHALT 6.57 5.67 20007
## 2 COARSE ASPHALT 6.64 5.75 1997
## 3 CONCRETE 6.14 5.11 692
## 4 GROOVED CONCRETE 6.73 5.8 371
## 5 OTHER 5.99 4.97 70
## [1] "Rd_Conditions"
## # A tibble: 4 x 4
## Rd_Conditions mean median n
## <fct> <dbl> <dbl> <int>
## 1 DRY 6.57 5.66 19262
## 2 ICE-SNOW-SLUSH 6.59 5.87 322
## 3 OTHER 6.23 5.63 134
## 4 WET 6.54 5.62 3419
## [1] "Light"
## # A tibble: 6 x 4
## Light mean median n
## <fct> <dbl> <dbl> <int>
## 1 DAYLIGHT 6.66 5.75 18262
## 2 DARK-LIT 6.24 5.4 3219
## 3 DARK-NOT-LIT 6.00 4.76 708
## 4 DAWN 6.32 5.44 140
## 5 DUSK 6.70 5.84 602
## 6 OTHER 5.36 4.33 206
## [1] "Weather"
## # A tibble: 5 x 4
## Weather mean median n
## <fct> <dbl> <dbl> <int>
## 1 CLEAR 6.57 5.65 17393
## 2 CLOUDY 6.52 5.68 3234
## 3 OTHER 5.65 4.84 85
## 4 RAIN 6.65 5.68 2230
## 5 SNOW 6.71 5.98 195
## [1] "Traffic_Control"
## # A tibble: 5 x 4
## Traffic_Control mean median n
## <fct> <dbl> <dbl> <int>
## 1 NONE 6.29 5.4 14028
## 2 OTHER 6.65 5.61 228
## 3 SIGNAL 7.04 6.14 6352
## 4 STOP-SIGN 6.95 6.14 2269
## 5 YIELD 6.60 5.56 260
## [1] "Work_Area"
## # A tibble: 2 x 4
## Work_Area mean median n
## <fct> <dbl> <dbl> <int>
## 1 NO 6.56 5.65 22823
## 2 YES 7.19 5.96 314Task 2
vars <- colnames(dat)[colnames(dat)!="Crash_Score"]
for (i in vars) {
plot <- ggplot(dat, aes(x=dat[,i])) + geom_bar() + labs(x=i) + theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(plot)
}
dat2<-dat
library(plyr)
var<-'Traffic_Control'
var.levels<-levels(dat2[,var])
dat2[,var]<-mapvalues(dat2[,var],var.levels,c('OTHER','OTHER','SIGNAL-STOP','SIGNAL-STOP','OTHER'))
table<-as.data.frame(table(dat2[,var]))
max<-which.max(table[,2])
level.name<-as.character(table[max,1])
dat2[,var]<-relevel(dat2[,var],ref=level.name)
table(dat2[,var])##
## OTHER SIGNAL-STOP
## 14516 8621var<-'Rd_Character'
dat2[,var]<-as.factor(dat2[,var])
var.levels<-levels(dat2[,var])
dat2[,var]<-mapvalues(dat2[,var],var.levels,c('STRAIGHT','CURVE','CURVE','CURVE','CURVE','STRAIGHT','STRAIGHT'))
table <- as.data.frame(table(dat2[,var]))
max <- which.max(table[,2])
level.name <- as.character(table[max,1])
dat2[,var] <- relevel(dat2[,var], ref = level.name)
table(dat2[,var])##
## STRAIGHT CURVE
## 21517 1620var<-'Rd_Feature'
dat2[,var]<-as.factor(dat2[,var])
var.levels<-levels(dat2[,var])
dat2[,var]<-mapvalues(dat2[,var],var.levels,c('OTHER','OTHER','INTERSECTION','OTHER','OTHER'))
table <- as.data.frame(table(dat2[,var]))
max <- which.max(table[,2])
level.name <- as.character(table[max,1])
dat2[,var] <- relevel(dat2[,var], ref = level.name)
table(dat2[,var])##
## OTHER INTERSECTION
## 16435 6702var<-'Time_of_Day'
dat2[,var]<-as.factor(dat2[,var])
var.levels<-levels(dat2[,var])
dat2[,var]<-mapvalues(dat2[,var],var.levels,c('OVERNIGHT','LATE-EARLY','DAYTIME','DAYTIME','DAYTIME','LATE-NIGHT'))
table <- as.data.frame(table(dat2[,var]))
max <- which.max(table[,2])
level.name <- as.character(table[max,1])
dat2[,var] <- relevel(dat2[,var], ref = level.name)
table(dat2[,var])##
## DAYTIME OVERNIGHT LATE-EARLY LATE-NIGHT
## 18345 808 1627 2357var<-'Rd_Surface'
dat2[,var] <- as.factor(dat2[,var])
var.levels <- levels(dat2[,var])
dat2[,var] <- mapvalues(dat2[,var],var.levels,c('ASPHALT','ASPHALT','OTHER','OTHER','OTHER'))
table <- as.data.frame(table(dat2[,var]))
max <- which.max(table[,2])
level.name <- as.character(table[max,1])
dat2[,var] <- relevel(dat2[,var], ref = level.name)
table(dat2[,var])##
## ASPHALT OTHER
## 22004 1133Task 3
datPCA<-dat[,c('Rd_Conditions','Light','Weather')]
library(caret)## Loading required package: latticevarsPCA<-colnames(datPCA)
for (var in varsPCA) {
datPCA[,var]<-as.character(datPCA[,var])
}
binarizer<-caret::dummyVars(paste('~',paste(varsPCA,collapse='+')),data=datPCA,fullRank=FALSE)
datPCAbin<-data.frame(predict(binarizer,newdata=datPCA))
head(datPCAbin)PCAweather <- prcomp(datPCAbin, center = TRUE, scale. = TRUE)
summary(PCAweather)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.829 1.3740 1.2796 1.2379 1.14429 1.03216 1.01236
## Proportion of Variance 0.223 0.1259 0.1092 0.1022 0.08729 0.07102 0.06833
## Cumulative Proportion 0.223 0.3489 0.4580 0.5602 0.64748 0.71851 0.78683
## PC8 PC9 PC10 PC11 PC12 PC13
## Standard deviation 1.0033 0.9174 0.79731 0.64583 0.54470 5.436e-15
## Proportion of Variance 0.0671 0.0561 0.04238 0.02781 0.01978 0.000e+00
## Cumulative Proportion 0.8539 0.9100 0.95241 0.98022 1.00000 1.000e+00
## PC14 PC15
## Standard deviation 2.2e-15 1.729e-15
## Proportion of Variance 0.0e+00 0.000e+00
## Cumulative Proportion 1.0e+00 1.000e+00PCAweather$rotation## PC1 PC2 PC3
## Rd_ConditionsDRY -0.51165971 0.03279495 -0.074984796
## Rd_ConditionsICE.SNOW.SLUSH 0.09037524 0.08506534 0.662448145
## Rd_ConditionsOTHER 0.05610221 0.18320852 0.103092721
## Rd_ConditionsWET 0.49654749 -0.10176327 -0.161823749
## LightDARK.LIT 0.11584644 0.52794265 -0.134963861
## LightDARK.NOT.LIT 0.05371675 0.19840327 -0.012771256
## LightDAWN 0.03037488 0.07312351 0.008834873
## LightDAYLIGHT -0.14979749 -0.66027088 0.122825366
## LightDUSK 0.04011811 0.17211754 -0.069299885
## LightOTHER 0.03196240 0.20556965 0.097572239
## WeatherCLEAR -0.45856690 0.18940018 -0.043504511
## WeatherCLOUDY 0.16796308 -0.22634633 0.028404961
## WeatherOTHER 0.05593982 0.14313571 0.095611440
## WeatherRAIN 0.43250589 -0.06252514 -0.190678603
## WeatherSNOW 0.09667013 0.07063727 0.650123103
## PC4 PC5 PC6
## Rd_ConditionsDRY 0.01484130 -0.18123640 -0.000304546
## Rd_ConditionsICE.SNOW.SLUSH 0.17309970 0.03263430 -0.003229391
## Rd_ConditionsOTHER -0.59102357 0.08185876 0.013871952
## Rd_ConditionsWET 0.05361910 0.16242361 -0.001579706
## LightDARK.LIT 0.20553848 -0.15082249 0.524023896
## LightDARK.NOT.LIT 0.08325274 -0.02460026 -0.666073761
## LightDAWN 0.04478967 -0.08625994 -0.148703516
## LightDAYLIGHT -0.11844823 0.19122929 0.064770906
## LightDUSK 0.03224585 -0.14753349 -0.501319635
## LightOTHER -0.48728603 0.09181326 -0.018210396
## WeatherCLEAR 0.08594496 0.35125976 -0.020935930
## WeatherCLOUDY -0.14707914 -0.76095953 0.031641146
## WeatherOTHER -0.49208702 0.06877513 0.035509159
## WeatherRAIN 0.09025035 0.35702863 -0.020451317
## WeatherSNOW 0.18605948 0.02838991 0.021438779
## PC7 PC8 PC9
## Rd_ConditionsDRY -0.015248814 -0.007849658 -0.003938895
## Rd_ConditionsICE.SNOW.SLUSH 0.028817357 -0.020239216 -0.011409533
## Rd_ConditionsOTHER -0.015819945 0.004441833 0.003261074
## Rd_ConditionsWET 0.009914945 0.013990907 0.007213656
## LightDARK.LIT -0.050731848 -0.067682247 -0.062223343
## LightDARK.NOT.LIT -0.624270732 -0.165555370 -0.071559483
## LightDAWN 0.005078405 0.969593680 -0.066722229
## LightDAYLIGHT 0.007612700 -0.009937129 -0.049517135
## LightDUSK 0.771261686 -0.147469720 -0.029538956
## LightOTHER -0.012772847 0.045476572 0.680552997
## WeatherCLEAR 0.027257542 0.030792065 0.019213521
## WeatherCLOUDY -0.080936296 -0.038591035 0.063032700
## WeatherOTHER 0.018306649 -0.019886896 -0.716703836
## WeatherRAIN 0.036838111 0.010225294 0.039016123
## WeatherSNOW 0.047153728 -0.018980562 0.018478936
## PC10 PC11 PC12
## Rd_ConditionsDRY -0.10561246 -0.174404093 0.408737115
## Rd_ConditionsICE.SNOW.SLUSH -0.02298359 0.667192980 0.139630559
## Rd_ConditionsOTHER 0.75494351 -0.019528178 0.075105047
## Rd_ConditionsWET -0.04271335 -0.032559601 -0.492233199
## LightDARK.LIT 0.04761367 0.005227977 0.014691286
## LightDARK.NOT.LIT 0.01708870 -0.022654370 0.002144386
## LightDAWN 0.01903033 -0.004732147 0.014857170
## LightDAYLIGHT 0.04151241 0.006230309 -0.009865851
## LightDUSK 0.03318236 0.002382120 -0.008017564
## LightOTHER -0.45890809 -0.004901907 -0.013905055
## WeatherCLEAR 0.06431667 0.122166890 -0.374060795
## WeatherCLOUDY 0.01221875 0.071474866 -0.067919098
## WeatherOTHER -0.44168092 -0.023803584 -0.006415880
## WeatherRAIN -0.02332117 -0.038841641 0.645813166
## WeatherSNOW 0.01730821 -0.707287146 -0.055286182
## PC13 PC14 PC15
## Rd_ConditionsDRY 0.39278871 -0.13988638 0.56181979
## Rd_ConditionsICE.SNOW.SLUSH 0.12322835 -0.04388611 0.17625793
## Rd_ConditionsOTHER 0.07982099 -0.02842716 0.11417082
## Rd_ConditionsWET 0.37329609 -0.13294435 0.53393879
## LightDARK.LIT 0.08964679 -0.53831132 -0.19670839
## LightDARK.NOT.LIT 0.04461418 -0.26789940 -0.09789514
## LightDAWN 0.02008866 -0.12062846 -0.04407975
## LightDAYLIGHT 0.10563622 -0.63432472 -0.23179337
## LightDUSK 0.04123618 -0.24761513 -0.09048291
## LightOTHER 0.02433305 -0.14611516 -0.05339304
## WeatherCLEAR 0.55120660 0.21321542 -0.33228056
## WeatherCLOUDY 0.44243475 0.17114075 -0.26671028
## WeatherOTHER 0.07719403 0.02985987 -0.04653441
## WeatherRAIN 0.37654617 0.14565401 -0.22699107
## WeatherSNOW 0.11664144 0.04511875 -0.07031426datPCAbin.std<-as.data.frame(scale(datPCAbin))
#dat2<-dat
dat2$WETorDRY<-0.51*datPCAbin.std$Rd_ConditionsDRY+0.5*datPCAbin.std$Rd_ConditionsWET-0.46*datPCAbin.std$WeatherCLEAR+0.43*datPCAbin.std$WeatherRAIN
summary(dat2$WETorDRY)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.7500 -0.3842 -0.3842 0.0000 -0.3842 2.1807dat<-dat2
dat$Rd_Conditions<-NULL
dat$Weather<-NULL
summary(dat)## Crash_Score year Month Time_of_Day
## Min. : 0.010 Min. :2014 Min. : 1.00 DAYTIME :18345
## 1st Qu.: 3.540 1st Qu.:2015 1st Qu.: 3.00 OVERNIGHT : 808
## Median : 5.660 Median :2016 Median : 7.00 LATE-EARLY: 1627
## Mean : 6.567 Mean :2016 Mean : 6.56 LATE-NIGHT: 2357
## 3rd Qu.: 8.600 3rd Qu.:2017 3rd Qu.:10.00
## Max. :53.070 Max. :2019 Max. :12.00
## Rd_Feature Rd_Character Rd_Class
## OTHER :16435 STRAIGHT:21517 STATE HWY:10603
## INTERSECTION: 6702 CURVE : 1620 OTHER : 9960
## US HWY : 2574
##
##
##
## Rd_Configuration Rd_Surface Light
## TWO-WAY-NO-MEDIAN :12076 ASPHALT:22004 DAYLIGHT :18262
## ONE-WAY : 1496 OTHER : 1133 DARK-LIT : 3219
## TWO-WAY-PROTECTED-MEDIAN : 2627 DARK-NOT-LIT: 708
## TWO-WAY-UNPROTECTED-MEDIAN: 6882 DAWN : 140
## UNKNOWN : 56 DUSK : 602
## OTHER : 206
## Traffic_Control Work_Area WETorDRY
## OTHER :14516 NO :22823 Min. :-1.7500
## SIGNAL-STOP: 8621 YES: 314 1st Qu.:-0.3842
## Median :-0.3842
## Mean : 0.0000
## 3rd Qu.:-0.3842
## Max. : 2.1807Task 4
ggplot(dat,aes(x=Rd_Character,y=log(Crash_Score),fill=Rd_Class))+
geom_boxplot()+
facet_wrap(~Rd_Character,scale="free")
ggplot(dat,aes(x=Traffic_Control,y=log(Crash_Score),fill=Rd_Feature))+
geom_boxplot()+
facet_wrap(~Traffic_Control,scale="free")
Task 5
dat$Month<-as.factor(dat$Month)
levels(dat$Month)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"library(caret)
set.seed(1234)
partition<-createDataPartition(dat$Crash_Score,list=FALSE,p=.75)
train<-dat[partition, ]
test<-dat[-partition, ]
print('TRAIN')## [1] "TRAIN"mean(train$Crash_Score)## [1] 6.561156print('TEST')## [1] "TEST"mean(test$Crash_Score)## [1] 6.58402GLMols<-glm(Crash_Score~.,family = gaussian(),data=train)
summary(GLMols)##
## Call:
## glm(formula = Crash_Score ~ ., family = gaussian(), data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.119 -2.970 -0.876 2.011 47.205
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 103.378410 44.597458 2.318
## year -0.047954 0.022115 -2.168
## Month2 0.228312 0.154849 1.474
## Month3 0.110909 0.156551 0.708
## Month4 0.003026 0.161885 0.019
## Month5 0.002956 0.153889 0.019
## Month6 -0.127018 0.158568 -0.801
## Month7 -0.100649 0.161562 -0.623
## Month8 -0.058145 0.155699 -0.373
## Month9 0.004877 0.158549 0.031
## Month10 0.167344 0.151545 1.104
## Month11 -0.023279 0.155254 -0.150
## Month12 0.331391 0.153274 2.162
## Time_of_DayOVERNIGHT -0.535530 0.190537 -2.811
## Time_of_DayLATE-EARLY -0.160209 0.130249 -1.230
## Time_of_DayLATE-NIGHT -0.096836 0.125186 -0.774
## Rd_FeatureINTERSECTION 0.335777 0.093745 3.582
## Rd_CharacterCURVE -0.210142 0.128721 -1.633
## Rd_ClassOTHER -0.543359 0.080441 -6.755
## Rd_ClassUS HWY 0.110509 0.131862 0.838
## Rd_ConfigurationONE-WAY -0.134412 0.141906 -0.947
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.089917 0.129186 0.696
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN -0.025951 0.084510 -0.307
## Rd_ConfigurationUNKNOWN 0.395609 0.635595 0.622
## Rd_SurfaceOTHER -0.264032 0.161973 -1.630
## LightDARK-LIT -0.317882 0.113729 -2.795
## LightDARK-NOT-LIT -0.666567 0.200865 -3.318
## LightDAWN -0.175518 0.435735 -0.403
## LightDUSK -0.183188 0.208614 -0.878
## LightOTHER -0.734078 0.348821 -2.104
## Traffic_ControlSIGNAL-STOP 0.235240 0.088518 2.658
## Work_AreaYES 0.317833 0.278329 1.142
## WETorDRY -0.005966 0.040202 -0.148
## Pr(>|t|)
## (Intercept) 0.020459 *
## year 0.030144 *
## Month2 0.140387
## Month3 0.478672
## Month4 0.985085
## Month5 0.984675
## Month6 0.423124
## Month7 0.533308
## Month8 0.708821
## Month9 0.975462
## Month10 0.269498
## Month11 0.880814
## Month12 0.030625 *
## Time_of_DayOVERNIGHT 0.004950 **
## Time_of_DayLATE-EARLY 0.218705
## Time_of_DayLATE-NIGHT 0.439218
## Rd_FeatureINTERSECTION 0.000342 ***
## Rd_CharacterCURVE 0.102585
## Rd_ClassOTHER 1.48e-11 ***
## Rd_ClassUS HWY 0.402009
## Rd_ConfigurationONE-WAY 0.343556
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.486420
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN 0.758790
## Rd_ConfigurationUNKNOWN 0.533672
## Rd_SurfaceOTHER 0.103099
## LightDARK-LIT 0.005194 **
## LightDARK-NOT-LIT 0.000907 ***
## LightDAWN 0.687094
## LightDUSK 0.379890
## LightOTHER 0.035353 *
## Traffic_ControlSIGNAL-STOP 0.007879 **
## Work_AreaYES 0.253498
## WETorDRY 0.882018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 17.97454)
##
## Null deviance: 315875 on 17353 degrees of freedom
## Residual deviance: 311337 on 17321 degrees of freedom
## AIC: 99418
##
## Number of Fisher Scoring iterations: 2print('AIC')## [1] "AIC"AIC(GLMols)## [1] 99418.44predict<-predict(GLMols,newdata=test,type='response')
print('RMSE')## [1] "RMSE"sqrt(sum((test$Crash_Score-predict)^2)/nrow(test))## [1] 4.285195GLMgamma<-glm(Crash_Score~.+Traffic_Control:Rd_Feature,family = Gamma(link = 'log'),data=train)
summary(GLMgamma)##
## Call:
## glm(formula = Crash_Score ~ . + Traffic_Control:Rd_Feature, family = Gamma(link = "log"),
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3179 -0.5544 -0.1435 0.2800 3.4186
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 16.4006471 6.8032872
## year -0.0071933 0.0033737
## Month2 0.0347164 0.0236225
## Month3 0.0159515 0.0238826
## Month4 0.0004506 0.0246954
## Month5 0.0004134 0.0234759
## Month6 -0.0194821 0.0241895
## Month7 -0.0162322 0.0246461
## Month8 -0.0089946 0.0237518
## Month9 -0.0008317 0.0241880
## Month10 0.0248085 0.0231181
## Month11 -0.0056385 0.0236839
## Month12 0.0510152 0.0233827
## Time_of_DayOVERNIGHT -0.0900418 0.0290688
## Time_of_DayLATE-EARLY -0.0244010 0.0198718
## Time_of_DayLATE-NIGHT -0.0141743 0.0190982
## Rd_FeatureINTERSECTION 0.0679312 0.0246778
## Rd_CharacterCURVE -0.0338970 0.0196827
## Rd_ClassOTHER -0.0819679 0.0123015
## Rd_ClassUS HWY 0.0180421 0.0201241
## Rd_ConfigurationONE-WAY -0.0215608 0.0216656
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.0140325 0.0197109
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN -0.0047520 0.0128943
## Rd_ConfigurationUNKNOWN 0.0740870 0.0969596
## Rd_SurfaceOTHER -0.0423269 0.0247223
## LightDARK-LIT -0.0505747 0.0173505
## LightDARK-NOT-LIT -0.1049072 0.0306462
## LightDAWN -0.0263254 0.0664712
## LightDUSK -0.0266138 0.0318238
## LightOTHER -0.1190162 0.0532127
## Traffic_ControlSIGNAL-STOP 0.0460243 0.0157969
## Work_AreaYES 0.0499734 0.0424600
## WETorDRY -0.0017194 0.0061328
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP -0.0278197 0.0298016
## t value Pr(>|t|)
## (Intercept) 2.411 0.01593 *
## year -2.132 0.03300 *
## Month2 1.470 0.14168
## Month3 0.668 0.50420
## Month4 0.018 0.98544
## Month5 0.018 0.98595
## Month6 -0.805 0.42060
## Month7 -0.659 0.51015
## Month8 -0.379 0.70492
## Month9 -0.034 0.97257
## Month10 1.073 0.28323
## Month11 -0.238 0.81183
## Month12 2.182 0.02914 *
## Time_of_DayOVERNIGHT -3.098 0.00195 **
## Time_of_DayLATE-EARLY -1.228 0.21949
## Time_of_DayLATE-NIGHT -0.742 0.45799
## Rd_FeatureINTERSECTION 2.753 0.00592 **
## Rd_CharacterCURVE -1.722 0.08506 .
## Rd_ClassOTHER -6.663 2.76e-11 ***
## Rd_ClassUS HWY 0.897 0.36998
## Rd_ConfigurationONE-WAY -0.995 0.31967
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.712 0.47653
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN -0.369 0.71248
## Rd_ConfigurationUNKNOWN 0.764 0.44482
## Rd_SurfaceOTHER -1.712 0.08690 .
## LightDARK-LIT -2.915 0.00356 **
## LightDARK-NOT-LIT -3.423 0.00062 ***
## LightDAWN -0.396 0.69208
## LightDUSK -0.836 0.40301
## LightOTHER -2.237 0.02532 *
## Traffic_ControlSIGNAL-STOP 2.913 0.00358 **
## Work_AreaYES 1.177 0.23923
## WETorDRY -0.280 0.77920
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP -0.933 0.35058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.4182877)
##
## Null deviance: 7311.8 on 17353 degrees of freedom
## Residual deviance: 7204.0 on 17320 degrees of freedom
## AIC: 93235
##
## Number of Fisher Scoring iterations: 5print('AIC')## [1] "AIC"AIC(GLMgamma)## [1] 93235.01predict<-predict(GLMgamma,newdata=test,type='response')
print('RMSE')## [1] "RMSE"sqrt(sum((test$Crash_Score-predict)^2)/nrow(test))## [1] 4.285252GLMnorm<-glm(Crash_Score~.+Traffic_Control:Rd_Feature,family = gaussian(link = 'log'),data=train)
summary(GLMnorm)##
## Call:
## glm(formula = Crash_Score ~ . + Traffic_Control:Rd_Feature, family = gaussian(link = "log"),
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.110 -2.966 -0.881 2.013 47.225
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 16.9230377 6.7748674
## year -0.0074520 0.0033596
## Month2 0.0346130 0.0234388
## Month3 0.0177666 0.0238523
## Month4 0.0004638 0.0248062
## Month5 0.0003880 0.0236406
## Month6 -0.0198460 0.0245978
## Month7 -0.0146273 0.0249741
## Month8 -0.0088130 0.0240056
## Month9 0.0021391 0.0242806
## Month10 0.0256225 0.0229781
## Month11 -0.0009390 0.0239097
## Month12 0.0488685 0.0230333
## Time_of_DayOVERNIGHT -0.0867608 0.0321548
## Time_of_DayLATE-EARLY -0.0239232 0.0199603
## Time_of_DayLATE-NIGHT -0.0154884 0.0195101
## Rd_FeatureINTERSECTION 0.0704499 0.0236039
## Rd_CharacterCURVE -0.0338879 0.0203990
## Rd_ClassOTHER -0.0832792 0.0123231
## Rd_ClassUS HWY 0.0190764 0.0194776
## Rd_ConfigurationONE-WAY -0.0244057 0.0227316
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.0131801 0.0190852
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN -0.0032949 0.0125138
## Rd_ConfigurationUNKNOWN 0.0515541 0.0993109
## Rd_SurfaceOTHER -0.0391858 0.0255640
## LightDARK-LIT -0.0464936 0.0176972
## LightDARK-NOT-LIT -0.1063687 0.0333406
## LightDAWN -0.0259004 0.0669502
## LightDUSK -0.0294692 0.0318988
## LightOTHER -0.1330737 0.0637014
## Traffic_ControlSIGNAL-STOP 0.0418374 0.0155124
## Work_AreaYES 0.0438690 0.0401251
## WETorDRY 0.0001240 0.0060803
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP -0.0306663 0.0284740
## t value Pr(>|t|)
## (Intercept) 2.498 0.01250 *
## year -2.218 0.02656 *
## Month2 1.477 0.13976
## Month3 0.745 0.45637
## Month4 0.019 0.98508
## Month5 0.016 0.98690
## Month6 -0.807 0.41978
## Month7 -0.586 0.55809
## Month8 -0.367 0.71353
## Month9 0.088 0.92980
## Month10 1.115 0.26483
## Month11 -0.039 0.96867
## Month12 2.122 0.03388 *
## Time_of_DayOVERNIGHT -2.698 0.00698 **
## Time_of_DayLATE-EARLY -1.199 0.23072
## Time_of_DayLATE-NIGHT -0.794 0.42728
## Rd_FeatureINTERSECTION 2.985 0.00284 **
## Rd_CharacterCURVE -1.661 0.09668 .
## Rd_ClassOTHER -6.758 1.44e-11 ***
## Rd_ClassUS HWY 0.979 0.32739
## Rd_ConfigurationONE-WAY -1.074 0.28300
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.691 0.48983
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN -0.263 0.79232
## Rd_ConfigurationUNKNOWN 0.519 0.60368
## Rd_SurfaceOTHER -1.533 0.12533
## LightDARK-LIT -2.627 0.00862 **
## LightDARK-NOT-LIT -3.190 0.00142 **
## LightDAWN -0.387 0.69886
## LightDUSK -0.924 0.35559
## LightOTHER -2.089 0.03672 *
## Traffic_ControlSIGNAL-STOP 2.697 0.00700 **
## Work_AreaYES 1.093 0.27427
## WETorDRY 0.020 0.98373
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP -1.077 0.28150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 17.97553)
##
## Null deviance: 315875 on 17353 degrees of freedom
## Residual deviance: 311335 on 17320 degrees of freedom
## AIC: 99420
##
## Number of Fisher Scoring iterations: 6print('AIC')## [1] "AIC"AIC(GLMnorm)## [1] 99420.35predict<-predict(GLMnorm,newdata=test,type='response')
print('RMSE')## [1] "RMSE"sqrt(sum((test$Crash_Score-predict)^2)/nrow(test))## [1] 4.285803library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectGLMgamma1<-glm(Crash_Score~1,family = Gamma(link='log'),data=train)
stepAIC(GLMgamma1,direction = 'forward',k=log(nrow(train)),scope=list(upper=GLMgamma,lower=GLMgamma1))## Start: AIC=93452.1
## Crash_Score ~ 1
##
## Df Deviance AIC
## + Rd_Class 2 7260.8 93351
## + Traffic_Control 1 7272.3 93368
## + Rd_Feature 1 7274.3 93373
## + Rd_Configuration 4 7293.7 93448
## + Time_of_Day 3 7298.4 93450
## <none> 7311.8 93452
## + Rd_Character 1 7309.6 93457
## + year 1 7310.3 93458
## + Rd_Surface 1 7310.4 93459
## + Work_Area 1 7311.0 93460
## + Light 5 7295.0 93461
## + WETorDRY 1 7311.6 93461
## + Month 11 7305.3 93544
##
## Step: AIC=93341.77
## Crash_Score ~ Rd_Class
##
## Df Deviance AIC
## + Rd_Feature 1 7242.4 93308
## + Traffic_Control 1 7243.8 93311
## + Time_of_Day 3 7246.2 93336
## <none> 7260.8 93342
## + Rd_Character 1 7258.5 93346
## + Rd_Surface 1 7258.6 93346
## + year 1 7259.0 93347
## + Light 5 7243.6 93349
## + Work_Area 1 7260.3 93350
## + WETorDRY 1 7260.8 93352
## + Rd_Configuration 4 7259.1 93377
## + Month 11 7255.0 93435
##
## Step: AIC=93304.48
## Crash_Score ~ Rd_Class + Rd_Feature
##
## Df Deviance AIC
## + Time_of_Day 3 7228.4 93300
## + Traffic_Control 1 7238.2 93304
## <none> 7242.4 93304
## + Rd_Character 1 7240.5 93310
## + year 1 7240.9 93311
## + Rd_Surface 1 7241.1 93311
## + Work_Area 1 7241.8 93313
## + WETorDRY 1 7242.4 93314
## + Light 5 7226.1 93314
## + Rd_Configuration 4 7241.7 93342
## + Month 11 7236.8 93399
##
## Step: AIC=93297.94
## Crash_Score ~ Rd_Class + Rd_Feature + Time_of_Day
##
## Df Deviance AIC
## <none> 7228.4 93298
## + Traffic_Control 1 7224.5 93298
## + year 1 7226.7 93304
## + Rd_Character 1 7226.9 93304
## + Rd_Surface 1 7227.3 93305
## + Work_Area 1 7227.9 93307
## + WETorDRY 1 7228.4 93308
## + Light 5 7220.8 93328
## + Rd_Configuration 4 7227.6 93335
## + Month 11 7222.8 93392##
## Call: glm(formula = Crash_Score ~ Rd_Class + Rd_Feature + Time_of_Day,
## family = Gamma(link = "log"), data = train)
##
## Coefficients:
## (Intercept) Rd_ClassOTHER Rd_ClassUS HWY
## 1.907945 -0.090945 0.003077
## Rd_FeatureINTERSECTION Time_of_DayOVERNIGHT Time_of_DayLATE-EARLY
## 0.075252 -0.136333 -0.032112
## Time_of_DayLATE-NIGHT
## -0.049669
##
## Degrees of Freedom: 17353 Total (i.e. Null); 17347 Residual
## Null Deviance: 7312
## Residual Deviance: 7228 AIC: 93240GLMgammaR<-glm(Crash_Score~Rd_Class+Rd_Feature+Time_of_Day+Traffic_Control,family=Gamma(link='log'),data=train)
summary(GLMgammaR)##
## Call:
## glm(formula = Crash_Score ~ Rd_Class + Rd_Feature + Time_of_Day +
## Traffic_Control, family = Gamma(link = "log"), data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3199 -0.5542 -0.1433 0.2784 3.3175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.89608 0.01024 185.147 < 2e-16 ***
## Rd_ClassOTHER -0.08340 0.01134 -7.352 2.03e-13 ***
## Rd_ClassUS HWY 0.00646 0.01696 0.381 0.703231
## Rd_FeatureINTERSECTION 0.05082 0.01415 3.590 0.000331 ***
## Time_of_DayOVERNIGHT -0.13431 0.02678 -5.016 5.32e-07 ***
## Time_of_DayLATE-EARLY -0.03169 0.01945 -1.629 0.103333
## Time_of_DayLATE-NIGHT -0.05011 0.01643 -3.050 0.002293 **
## Traffic_ControlSIGNAL-STOP 0.04060 0.01337 3.037 0.002396 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.4186809)
##
## Null deviance: 7311.8 on 17353 degrees of freedom
## Residual deviance: 7224.5 on 17346 degrees of freedom
## AIC: 93236
##
## Number of Fisher Scoring iterations: 5print('AIC')## [1] "AIC"AIC(GLMgammaR)## [1] 93235.58predict<-predict(GLMgammaR,newdata=test,type='response')
print('RMSE')## [1] "RMSE"sqrt(sum((test$Crash_Score-predict)^2)/nrow(test))## [1] 4.284678plot(GLMgammaR)
Task 8
GLMgammaRdat<-glm(Crash_Score ~ Rd_Class + Rd_Feature + Time_of_Day+Traffic_Control,family = Gamma(link='log'),data=dat)
summary(GLMgammaRdat)##
## Call:
## glm(formula = Crash_Score ~ Rd_Class + Rd_Feature + Time_of_Day +
## Traffic_Control, family = Gamma(link = "log"), data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3222 -0.5544 -0.1428 0.2770 3.3252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.890687 0.008846 213.742 < 2e-16 ***
## Rd_ClassOTHER -0.081323 0.009822 -8.280 < 2e-16 ***
## Rd_ClassUS HWY 0.018101 0.014698 1.232 0.21813
## Rd_FeatureINTERSECTION 0.053271 0.012258 4.346 1.39e-05 ***
## Time_of_DayOVERNIGHT -0.117653 0.023295 -5.051 4.44e-07 ***
## Time_of_DayLATE-EARLY -0.050126 0.016803 -2.983 0.00286 **
## Time_of_DayLATE-NIGHT -0.044273 0.014174 -3.124 0.00179 **
## Traffic_ControlSIGNAL-STOP 0.049265 0.011571 4.257 2.08e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Gamma family taken to be 0.4192278)
##
## Null deviance: 9740.7 on 23136 degrees of freedom
## Residual deviance: 9615.7 on 23129 degrees of freedom
## AIC: 124305
##
## Number of Fisher Scoring iterations: 5Task 9
library(glmnet)## Loading required package: Matrix## Loading required package: foreach## Loaded glmnet 2.0-16set.seed(42)
X<-model.matrix(Crash_Score~.+Traffic_Control:Rd_Feature,train)
m<-cv.glmnet(x=X,y=train$Crash_Score,family='gaussian',alpha=1)
plot(m)
m.best<-glmnet(x=X,y=train$Crash_Score,family='gaussian',lambda=m$lambda.min,alpha=1)
X.test<-model.matrix(Crash_Score~.+Traffic_Control:Rd_Feature,test)
m.best$beta## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) .
## year -0.035230776
## Month2 0.157206654
## Month3 0.046209463
## Month4 .
## Month5 .
## Month6 -0.072368785
## Month7 -0.041073747
## Month8 -0.006126929
## Month9 .
## Month10 0.109130441
## Month11 .
## Month12 0.263876244
## Time_of_DayOVERNIGHT -0.481383043
## Time_of_DayLATE-EARLY -0.090698959
## Time_of_DayLATE-NIGHT -0.070069854
## Rd_FeatureINTERSECTION 0.310714390
## Rd_CharacterCURVE -0.157376229
## Rd_ClassOTHER -0.526174010
## Rd_ClassUS HWY 0.021778137
## Rd_ConfigurationONE-WAY -0.063368334
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.060092421
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN .
## Rd_ConfigurationUNKNOWN 0.044014946
## Rd_SurfaceOTHER -0.143226618
## LightDARK-LIT -0.270482724
## LightDARK-NOT-LIT -0.554771485
## LightDAWN .
## LightDUSK -0.058549755
## LightOTHER -0.559246300
## Traffic_ControlSIGNAL-STOP 0.224366735
## Work_AreaYES 0.174188162
## WETorDRY .
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP .m.best.predict<-predict(m.best,newx=X.test)
rmse<-sqrt(sum((m.best.predict-test$Crash_Score)^2)/nrow(test))
rmse## [1] 4.284406set.seed(42)
X<-model.matrix(Crash_Score~.+Traffic_Control:Rd_Feature,train)
m<-cv.glmnet(x=X,y=train$Crash_Score,family='gaussian',alpha=0)
plot(m)
m.best<-glmnet(x=X,y=train$Crash_Score,family='gaussian',lambda=m$lambda.min,alpha=0)
X.test<-model.matrix(Crash_Score~.+Traffic_Control:Rd_Feature,test)
m.best$beta## 34 x 1 sparse Matrix of class "dgCMatrix"
## s0
## (Intercept) .
## year -0.03760358
## Month2 0.15344261
## Month3 0.06397486
## Month4 -0.01799748
## Month5 -0.01975586
## Month6 -0.12436708
## Month7 -0.10135990
## Month8 -0.06693177
## Month9 -0.01624658
## Month10 0.11120885
## Month11 -0.05227089
## Month12 0.23526357
## Time_of_DayOVERNIGHT -0.47502496
## Time_of_DayLATE-EARLY -0.12033859
## Time_of_DayLATE-NIGHT -0.11155756
## Rd_FeatureINTERSECTION 0.24403447
## Rd_CharacterCURVE -0.16884312
## Rd_ClassOTHER -0.41763915
## Rd_ClassUS HWY 0.10355680
## Rd_ConfigurationONE-WAY -0.11116457
## Rd_ConfigurationTWO-WAY-PROTECTED-MEDIAN 0.10725507
## Rd_ConfigurationTWO-WAY-UNPROTECTED-MEDIAN 0.03625030
## Rd_ConfigurationUNKNOWN 0.26568994
## Rd_SurfaceOTHER -0.20134860
## LightDARK-LIT -0.24001818
## LightDARK-NOT-LIT -0.50608543
## LightDAWN -0.13232862
## LightDUSK -0.12193123
## LightOTHER -0.60898119
## Traffic_ControlSIGNAL-STOP 0.21377149
## Work_AreaYES 0.26274006
## WETorDRY -0.00117400
## Rd_FeatureINTERSECTION:Traffic_ControlSIGNAL-STOP 0.08556195m.best.predict<-predict(m.best,newx=X.test)
rmse<-sqrt(sum((m.best.predict-test$Crash_Score)^2)/nrow(test))
rmse## [1] 4.284918Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Cmd+Option+I.
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